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Paper · 33096023

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.

Kuenzi BM and 7 others. Cancer Cell, 2020. Cited by 349 papers, by iCite.

11 sourcespapers-2026-11-12-0.1.0

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ConductScience Library. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Papers, record 33096023. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/33096023

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Paper

PubMed's record of the paper, with iCite's citation figures. No abstract is shown.

Title
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.
PubMed
Journal
Cancer Cell
PubMed
Year
2020
PubMed
Publication types
Journal Article, Research Support, N.I.H., Extramural
PubMed
Citations, by iCite
349
iCite
Relative citation ratio, by iCite
13.9
iCite
Revised in PubMed
2023-11-10
PubMed

Authors8

The authors in the order PubMed's record lists them, with the ORCID iDs it states.

OrderAuthorForenameKindORCID iD
1Kuenzi BMBrent M
2Park JJisoo
3Fong SHSamson H
4Sanchez KSKyle S
5Lee JJohn
6Kreisberg JFJason F
7Ma JJianzhu
8Ideker TTrey

Source PubMed

Topics11

NLM's MeSH indexing of the paper, its major topics first.

MeSH termMajor
Antineoplastic AgentsMajor topic
Computational BiologyMajor topic
NeoplasmsMajor topic
Cell Line, Tumor
Databases, Factual
Deep Learning
Drug Screening Assays, Antitumor
Drug Synergism
Genotype
Humans
Patient-Specific Modeling

Source PubMed

Grants8

Each grant PubMed's record writes, matched to the NIH project NIH's grant and paper links name for this paper, and each of those links the record does not write.

Grant, as the record writes itAgencyCountryNIH projectStated bySource
R50 CA243885NCI NIH HHSUnited StatesR50CA243885PubMed and NIHNIH RePORTER
P41 GM103504NIGMS NIH HHSUnited StatesP41GM103504PubMed and NIHNIH RePORTER
R01 ES014811NIEHS NIH HHSUnited StatesR01ES014811PubMed and NIHNIH RePORTER
F99 CA212456NCI NIH HHSUnited StatesF99CA212456PubMed and NIHNIH RePORTER
P30 CA023100NCI NIH HHSUnited StatesP30CA023100PubMed and NIHNIH RePORTER
K00 CA212456NCI NIH HHSUnited StatesF99CA212456PubMed and NIHNIH RePORTER
U54 CA209891NCI NIH HHSUnited StatesU54CA209891PubMed and NIHNIH RePORTER
K00CA212456NIHNIH RePORTER

Trials0

The trials whose ClinicalTrials.gov record lists the paper, and the trial numbers the paper's PubMed record names.

None in this release.

Comments and corrections2

PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.

KindPMIDTitleIn PapersSource
Comment in33,096,022Biologically Informed Neural Networks Predict Drug Responses.https://conductscience.org/library/papers/33096022PubMed
Comment in33,239,267Artificial Intelligence Predicts Drug Response.https://conductscience.org/library/papers/33239267PubMed

References78

The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.

OrderPMIDTitleJournalYearIn PapersSource
123,282,027Comparison and evaluation of pathway-level aggregation methods of gene expression data.BMC Genomics2012https://conductscience.org/library/papers/23282027PubMed
230,770,740EGFR signaling confers resistance to BET inhibition in hepatocellular carcinoma through stabilizing oncogenic MYC.J Exp Clin Cancer Res2019https://conductscience.org/library/papers/30770740PubMed
326,700,849A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.Mol Syst Biol2015https://conductscience.org/library/papers/26700849PubMed
425,867,045Mode of action and pharmacogenomic biomarkers for exceptional responders to didemnin B.Nat Chem Biol2015https://conductscience.org/library/papers/25867045PubMed
530,679,640Off-target based drug repurposing opportunities for tivantinib in acute myeloid leukemia.Sci Rep2019https://conductscience.org/library/papers/30679640PubMed
625,517,437Prediction of antibiotic resistance by gene expression profiles.Nat Commun2014https://conductscience.org/library/papers/25517437PubMed
730,188,539Deep learning in biomedicine.Nat Biotechnol2018https://conductscience.org/library/papers/30188539PubMed
829,236,940Polyclonal RB1 mutations and acquired resistance to CDK 4/6 inhibitors in patients with metastatic breast cancer.Ann Oncol2018https://conductscience.org/library/papers/29236940PubMed
928,639,053Managing the genomic revolution in cancer diagnostics.Virchows Arch2017https://conductscience.org/library/papers/28639053PubMed
1030,943,338Machine Learning in Medicine.N Engl J Med2019https://conductscience.org/library/papers/30943338PubMed

1 to 10 of 78

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Cited by264

The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 349 citations.

PMIDTitleJournalYearIn PapersSource
42,974,497Hepatobiliary cancers digital twins: Integrating artificial intelligence-driven multi-omics with organoid dynamics.J Transl Int Med2026https://conductscience.org/library/papers/42974497PubMed
42,938,347Integration of Multi-Omics in AI-Based Models for Predictive Oncology.Genes (Basel)2026https://conductscience.org/library/papers/42938347PubMed
42,936,269xNNPCD identifies regulators of programmed cell death by integrating perturbation transcriptomes with cancer dependency profiles.NAR Cancer2026https://conductscience.org/library/papers/42936269PubMed
42,921,250Estimating Individualized Survival Benefit of Consolidative Metastasis-Directed Radiotherapy Following Chemoimmunotherapy in Nasopharyngeal Carcinoma With Exclusive Distant Metastasis.MedComm (2020)2026https://conductscience.org/library/papers/42921250PubMed
42,868,205Interpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes.PLoS Comput Biol2026https://conductscience.org/library/papers/42868205PubMed
42,850,682Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Adv Sci (Weinh)2026https://conductscience.org/library/papers/42850682PubMed
42,823,735Artificial intelligence in oncology: linking biological discovery to clinical utility.Mol Cancer2026https://conductscience.org/library/papers/42823735PubMed
42,781,244Leveraging uncertainty estimates for drug response prediction in cancer cell lines.Bioinform Adv2026https://conductscience.org/library/papers/42781244PubMed
42,778,845DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdiscip Sci2026https://conductscience.org/library/papers/42778845PubMed
42,756,558Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Front Oncol2026https://conductscience.org/library/papers/42756558PubMed

1 to 10 of 264

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Genes35

The genes the Gene search release ties to the paper: Swiss-Prot's references first, then PubTator's text mining.

GeneNCBI Gene IDLinked byIn GenesSource
AAVS117PubTator's text mininghttps://conductscience.org/library/genes/17NCBI Gene
ABL125PubTator's text mininghttps://conductscience.org/library/genes/25NCBI Gene
AKT1207PubTator's text mininghttps://conductscience.org/library/genes/207NCBI Gene
ALK238PubTator's text mininghttps://conductscience.org/library/genes/238NCBI Gene
APC324PubTator's text mininghttps://conductscience.org/library/genes/324NCBI Gene
BCR613PubTator's text mininghttps://conductscience.org/library/genes/613NCBI Gene
BRAF673PubTator's text mininghttps://conductscience.org/library/genes/673NCBI Gene
CTNNB11,499PubTator's text mininghttps://conductscience.org/library/genes/1499NCBI Gene
DNER92,737PubTator's text mininghttps://conductscience.org/library/genes/92737NCBI Gene
DUSP11,843PubTator's text mininghttps://conductscience.org/library/genes/1843NCBI Gene

1 to 10 of 35

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Drugs0

The drugs whose trials, in the Pipelines release, list the paper.

None in this release.

Data0

The data bank entries PubMed's record names. Trial numbers are under Trials.

None in this release.

Data dates

The date of each source this release read.

PubMed
2026-11-06
PubMed
iCite
2026-11-05
iCite
PMC's ID file
2026-11-05
PMC's ID file
NIH RePORTER's grant and paper links
2026-11-01
NIH RePORTER's grant and paper links
Trials
2026-11-12
Gene search
2026-11-11
Pipelines
2026-09-18

Sources 11

  • ChEMBL, through Open TargetsCC BY-SA 3.0
  • ClinicalTrials.govNo license stated
  • HGNCCC0
  • iCiteCC BY 4.0
  • NCBI GenePublic domain (US government)
  • NIH RePORTER's grant and paper linksPublic domain
  • Open TargetsCC0 1.0
  • PMC's ID filePublic domain (US government)
  • PubMedNLM's terms
  • PubTator 3Public domain
  • UniProtCC BY 4.0
Each source's terms
  • ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
  • ClinicalTrials.govnone stated
  • HGNCCC0: https://www.genenames.org/about/license/
  • iCiteCC BY 4.0
  • NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
  • NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
  • Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
  • PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
  • PubMedNLM's terms
  • PubTator 3Public domain
  • UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license

ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.

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